TY - JOUR A1 - Schütt, Heiko Herbert A1 - Rothkegel, Lars Oliver Martin A1 - Trukenbrod, Hans Arne A1 - Reich, Sebastian A1 - Wichmann, Felix A. A1 - Engbert, Ralf T1 - Likelihood-based parameter estimation and comparison of dynamical cognitive models JF - Psychological Review N2 - Dynamical models of cognition play an increasingly important role in driving theoretical and experimental research in psychology. Therefore, parameter estimation, model analysis and comparison of dynamical models are of essential importance. In this article, we propose a maximum likelihood approach for model analysis in a fully dynamical framework that includes time-ordered experimental data. Our methods can be applied to dynamical models for the prediction of discrete behavior (e.g., movement onsets); in particular, we use a dynamical model of saccade generation in scene viewing as a case study for our approach. For this model, the likelihood function can be computed directly by numerical simulation, which enables more efficient parameter estimation including Bayesian inference to obtain reliable estimates and corresponding credible intervals. Using hierarchical models inference is even possible for individual observers. Furthermore, our likelihood approach can be used to compare different models. In our example, the dynamical framework is shown to outperform nondynamical statistical models. Additionally, the likelihood based evaluation differentiates model variants, which produced indistinguishable predictions on hitherto used statistics. Our results indicate that the likelihood approach is a promising framework for dynamical cognitive models. KW - likelihood KW - model fitting KW - dynamical model KW - eye movements KW - model comparison Y1 - 2017 U6 - https://doi.org/10.1037/rev0000068 SN - 0033-295X SN - 1939-1471 VL - 124 IS - 4 SP - 505 EP - 524 PB - American Psychological Association CY - Washington ER - TY - JOUR A1 - Schütt, Heiko Herbert A1 - Rothkegel, Lars Oliver Martin A1 - Trukenbrod, Hans Arne A1 - Engbert, Ralf A1 - Wichmann, Felix A. T1 - Disentangling bottom-up versus top-down and low-level versus high-level influences on eye movements over time JF - Journal of vision N2 - Bottom-up and top-down as well as low-level and high-level factors influence where we fixate when viewing natural scenes. However, the importance of each of these factors and how they interact remains a matter of debate. Here, we disentangle these factors by analyzing their influence over time. For this purpose, we develop a saliency model that is based on the internal representation of a recent early spatial vision model to measure the low-level, bottom-up factor. To measure the influence of high-level, bottom-up features, we use a recent deep neural network-based saliency model. To account for top-down influences, we evaluate the models on two large data sets with different tasks: first, a memorization task and, second, a search task. Our results lend support to a separation of visual scene exploration into three phases: the first saccade, an initial guided exploration characterized by a gradual broadening of the fixation density, and a steady state that is reached after roughly 10 fixations. Saccade-target selection during the initial exploration and in the steady state is related to similar areas of interest, which are better predicted when including high-level features. In the search data set, fixation locations are determined predominantly by top-down processes. In contrast, the first fixation follows a different fixation density and contains a strong central fixation bias. Nonetheless, first fixations are guided strongly by image properties, and as early as 200 ms after image onset, fixations are better predicted by high-level information. We conclude that any low-level, bottom-up factors are mainly limited to the generation of the first saccade. All saccades are better explained when high-level features are considered, and later, this high-level, bottom-up control can be overruled by top-down influences. KW - saliency KW - fixations KW - natural scenes KW - visual search KW - eye movements Y1 - 2019 U6 - https://doi.org/10.1167/19.3.1 SN - 1534-7362 VL - 19 IS - 3 PB - Association for Research in Vision and Opthalmology CY - Rockville ER - TY - GEN A1 - Schütt, Heiko Herbert A1 - Rothkegel, Lars Oliver Martin A1 - Trukenbrod, Hans Arne A1 - Engbert, Ralf A1 - Wichmann, Felix A. T1 - Predicting fixation densities over time from early visual processing T2 - Perception N2 - Bottom-up saliency is often cited as a factor driving the choice of fixation locations of human observers, based on the (partial) success of saliency models to predict fixation densities in free viewing. However, these observations are only weak evidence for a causal role of bottom-up saliency in natural viewing behaviour. To test bottom-up saliency more directly, we analyse the performance of a number of saliency models---including our own saliency model based on our recently published model of early visual processing (Schütt & Wichmann, 2017, JoV)---as well as the theoretical limits for predictions over time. On free viewing data our model performs better than classical bottom-up saliency models, but worse than the current deep learning based saliency models incorporating higher-level information like knowledge about objects. However, on search data all saliency models perform worse than the optimal image independent prediction. We observe that the fixation density in free viewing is not stationary over time, but changes over the course of a trial. It starts with a pronounced central fixation bias on the first chosen fixation, which is nonetheless influenced by image content. Starting with the 2nd to 3rd fixation, the fixation density is already well predicted by later densities, but more concentrated. From there the fixation distribution broadens until it reaches a stationary distribution around the 10th fixation. Taken together these observations argue against bottom-up saliency as a mechanistic explanation for eye movement control after the initial orienting reaction in the first one to two saccades, although we confirm the predictive value of early visual representations for fixation locations. The fixation distribution is, first, not well described by any stationary density, second, is predicted better when including object information and, third, is badly predicted by any saliency model in a search task. Y1 - 2019 SN - 0301-0066 SN - 1468-4233 VL - 48 SP - 64 EP - 65 PB - Sage Publ. CY - London ER - TY - JOUR A1 - Rothkegel, Lars Oliver Martin A1 - Trukenbrod, Hans Arne A1 - Schütt, Heiko Herbert A1 - Wichmann, Felix A. A1 - Engbert, Ralf T1 - Influence of initial fixation position in scene viewing JF - Vision research : an international journal for functional aspects of vision. KW - Visual scanpath KW - Visual attention KW - Inhibition of return KW - Eye movements KW - Saliency Y1 - 2016 U6 - https://doi.org/10.1016/j.visres.2016.09.012 SN - 0042-6989 SN - 1878-5646 VL - 129 SP - 33 EP - 49 PB - Elsevier CY - Oxford ER - TY - JOUR A1 - Rothkegel, Lars Oliver Martin A1 - Schütt, Heiko Herbert A1 - Trukenbrod, Hans Arne A1 - Wichmann, Felix A. A1 - Engbert, Ralf T1 - Searchers adjust their eye-movement dynamics to target characteristics in natural scenes JF - Scientific reports N2 - When searching a target in a natural scene, it has been shown that both the target’s visual properties and similarity to the background influence whether and how fast humans are able to find it. So far, it was unclear whether searchers adjust the dynamics of their eye movements (e.g., fixation durations, saccade amplitudes) to the target they search for. In our experiment, participants searched natural scenes for six artificial targets with different spatial frequency content throughout eight consecutive sessions. High-spatial frequency targets led to smaller saccade amplitudes and shorter fixation durations than low-spatial frequency targets if target identity was known. If a saccade was programmed in the same direction as the previous saccade, fixation durations and successive saccade amplitudes were not influenced by target type. Visual saliency and empirical fixation density at the endpoints of saccades which maintain direction were comparatively low, indicating that these saccades were less selective. Our results suggest that searchers adjust their eye movement dynamics to the search target efficiently, since previous research has shown that low-spatial frequencies are visible farther into the periphery than high-spatial frequencies. We interpret the saccade direction specificity of our effects as an underlying separation into a default scanning mechanism and a selective, target-dependent mechanism. Y1 - 2019 U6 - https://doi.org/10.1038/s41598-018-37548-w SN - 2045-2322 VL - 9 PB - Nature Publ. Group CY - London ER - TY - GEN A1 - Geirhos, Robert A1 - Temme, Carlos R. Medina A1 - Rauber, Jonas A1 - Schütt, Heiko Herbert A1 - Bethge, Matthias A1 - Wichmann, Felix A. T1 - Generalisation in humans and deep neural networks T2 - Proceedings of the 32nd International Conference on Neural Information Processing Systems N2 - We compare the robustness of humans and current convolutional deep neural networks (DNNs) on object recognition under twelve different types of image degradations. First, using three well known DNNs (ResNet-152, VGG-19, GoogLeNet) we find the human visual system to be more robust to nearly all of the tested image manipulations, and we observe progressively diverging classification error-patterns between humans and DNNs when the signal gets weaker. Secondly, we show that DNNs trained directly on distorted images consistently surpass human performance on the exact distortion types they were trained on, yet they display extremely poor generalisation abilities when tested on other distortion types. For example, training on salt-and-pepper noise does not imply robustness on uniform white noise and vice versa. Thus, changes in the noise distribution between training and testing constitutes a crucial challenge to deep learning vision systems that can be systematically addressed in a lifelong machine learning approach. Our new dataset consisting of 83K carefully measured human psychophysical trials provide a useful reference for lifelong robustness against image degradations set by the human visual system. Y1 - 2018 SN - 1049-5258 VL - 31 SP - 7549 EP - 7561 PB - Curran Associates Inc. CY - Red Hook ER -